Miscellaneous cleanup

examples/calculations/Sounding_Calculations.py

@@ -37,14 +37,11 @@ def effective_layer(p, t, td, h, height_layer=False):
       - pbot/hbot, ptop/htop: pressure/height of the bottom level,
                               pressure/height of the top level
     """
-    from metpy.calc import cape_cin, parcel_profile
-    from metpy.units import units
-
     pbot = None
 
     for i in range(p.shape[0]):
-        prof = parcel_profile(p[i:], t[i], td[i])
-        sbcape, sbcin = cape_cin(p[i:], t[i:], td[i:], prof)
+        prof = mpcalc.parcel_profile(p[i:], t[i], td[i])
+        sbcape, sbcin = mpcalc.cape_cin(p[i:], t[i:], td[i:], prof)
         if sbcape >= 100 * units('J/kg') and sbcin > -250 * units('J/kg'):
             pbot = p[i]
             hbot = h[i]
@@ -54,8 +51,8 @@ def effective_layer(p, t, td, h, height_layer=False):
         return None, None
 
     for i in range(bot_idx + 1, p.shape[0]):
-        prof = parcel_profile(p[i:], t[i], td[i])
-        sbcape, sbcin = cape_cin(p[i:], t[i:], td[i:], prof)
+        prof = mpcalc.parcel_profile(p[i:], t[i], td[i])
+        sbcape, sbcin = mpcalc.cape_cin(p[i:], t[i:], td[i:], prof)
         if sbcape < 100 * units('J/kg') or sbcin < -250 * units('J/kg'):
             ptop = p[i]
             htop = h[i]

examples/plots/Mesonet_Stationplot.py

@@ -31,7 +31,7 @@
 
 # Current observations can be downloaded here:
 # https://www.mesonet.org/index.php/weather/category/past_data_files
-data = pd.read_csv(get_test_data('mesonet_sample.txt'), na_values=' ')
+data = pd.read_csv(get_test_data('mesonet_sample.txt', as_file_obj=False), na_values=' ')
 
 # Drop stations with missing values of data we want
 data = data.dropna(how='any', subset=['PRES', 'TAIR', 'TDEW', 'WDIR', 'WSPD'])

examples/plots/Simplified_Image_Plot.py

@@ -14,10 +14,10 @@
 import xarray as xr
 
 from metpy.cbook import get_test_data
-from metpy.io import GiniFile
 from metpy.plots import ImagePlot, MapPanel, PanelContainer
 
-data = xr.open_dataset(GiniFile(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini')))
+data = xr.open_dataset(get_test_data('NHEM-MULTICOMP_1km_IR_20151208_2100.gini',
+                                     as_file_obj=False))
 
 img = ImagePlot()
 img.data = data

examples/plots/nhc_wind_probabilities.py

@@ -22,7 +22,8 @@
 
 ###########################
 # Read in the shapefile file containing the wind probabilities.
-wind_data = geopandas.read_file(get_test_data('nhc_wind_prob_2021082012.zip'))
+wind_data = geopandas.read_file(get_test_data('nhc_wind_prob_2021082012.zip',
+                                              as_file_obj=False))
 
 ###########################
 # Add the color scheme to the GeoDataFrame. This is the same color scheme used by the National
@@ -33,7 +34,7 @@
 
 ###########################
 # Read in the shapefile file containing the cities.
-cities = geopandas.read_file(get_test_data('us_cities.zip'))
+cities = geopandas.read_file(get_test_data('us_cities.zip', as_file_obj=False))
 
 ###########################
 # There are thousands of cities in the United States. We choose a few cities here that we want

examples/plots/spc_convective_outlook.py

@@ -18,7 +18,8 @@
 
 ###########################
 # Read in the geoJSON file containing the convective outlook.
-day1_outlook = geopandas.read_file(get_test_data('spc_day1otlk_20210317_1200_lyr.geojson'))
+day1_outlook = geopandas.read_file(get_test_data('spc_day1otlk_20210317_1200_lyr.geojson',
+                                                 as_file_obj=False))
 
 ###########################
 # Preview the data.

src/metpy/io/nexrad.py

@@ -984,7 +984,7 @@ class LegacyMapper(DataMapper):
 
     def __init__(self, prod):
         """Initialize the values and labels from the product."""
-        # Don't worry about super() since we're using our own lut assembled sequentially
+        super().__init__()
         self.labels = []
         self.lut = []
         for t in prod.thresholds:

tests/plots/test_util.py

@@ -54,8 +54,8 @@ def test_add_timestamp_xarray():
     """Test that add_timestamp can work with xarray datetime accessor."""
     with autoclose_figure() as fig:
         ax = fig.add_subplot(1, 1, 1)
-        ds = xr.open_dataset(get_test_data('AK-REGIONAL_8km_3.9_20160408_1445.gini'),
-                             engine='gini')
+        ds = xr.open_dataset(get_test_data('AK-REGIONAL_8km_3.9_20160408_1445.gini',
+                                           as_file_obj=False), engine='gini')
         txt = add_timestamp(ax, ds.time.dt, pretext='')
         assert txt.get_text() == '2016-04-08T14:45:20Z'